Title: A novel mobile charging planning method based on swarm reinforcement learning in wireless sensor networks

Authors: Zengwei Lyu; Pengfei Li; Zhenchun Wei; Juan Xu; Lei Shi

Addresses: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

Abstract: In order to solve the problem of energy supplement in large-scale wireless sensor networks (WSNs), this paper investigates the charging planning problem by introduced multiple wireless charger equipment (WCE). We first established the optimisation model of the multi-WCE charging planning problem to minimise the total charging time and the total energy consumption of the WCE. Then, the problem is modelled as a reinforcement learning process, and the time step, state space, action space, state transfer function and reward function are designed. Moreover, based on the idea of swarm intelligence optimisation method, a multi-learners' strategy is introduced to enable multi-learners to parallel learning, so as to accelerate the solution finding speed. Therefore, a discrete firework Q-learning algorithm is proposed to solve the problem. Experiments show that the proposed algorithm outperforms the baseline algorithms in different network scales.

Keywords: wireless sensor network; WSN; mobile charging planning; reinforcement learning; firework algorithm.

DOI: 10.1504/IJSNET.2023.129813

International Journal of Sensor Networks, 2023 Vol.41 No.3, pp.176 - 188

Received: 12 May 2022
Accepted: 16 Jan 2023
Published online: 30 Mar 2023 *

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